Multimodal Estimation of Distribution Algorithms
Multimodal Estimation of Distribution Algorithms
复制标题
多模态分布估计算法
DOI：
10.1109/tcyb.2016.2523000
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发表时间：
2017
影响因子：
11.8
通讯作者：
Zhang Jun
中科院分区：
文献类型：
--
作者：
Yang Qiang;Chen Wei-Neng;Li Yun;Chen C. L. Philip;Xu Xiang-Min;Zhang Jun
利用分布估计算法（EDA）在保持高多样性方面的优势，本文提出了一种多模态EDA。结合拥挤和物种形成的聚类策略，该算法的两个版本被开发出来，它们在小生境水平上运行。然后，这两个算法配备了三个不同的技术：1）一个动态的集群大小的策略; 2）高斯和柯西分布的交替使用，以产生后代;和3）自适应局部搜索。动态集群大小提供了一个潜在的平衡之间的勘探和开发，并降低了集群规模的小生境方法的敏感性。利用高斯分布和柯西分布的优势，通过交替使用这两种分布，在生态位水平上产生后代。这种利用还可能在勘探和开采之间实现平衡。此外，解决方案的准确性提高，通过一个新的局部搜索方案概率进行周围的种子的小生境的概率确定自适应根据这些种子的适应值。在20个基准多模态问题上进行的大量实验证实，这两种算法与几种最先进的多模态算法相比，都可以达到有竞争力的性能，这是由非参数测试支持的。特别是，所提出的算法是非常有前途的复杂问题与许多局部最优解。
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.